We randomly sample \(4~123\)  sentences from the Toronto BookCorpus [1]}—a collection of English books of various genres—for computing Mantel tests using Levenshtein distance, both raw and normalized, as the textual distance.
We repeat the procedure 5 times before averaging results.
We use the same operations as previously to control for synonyms and stop-words; i.e., we test whether removing stop-words altogether and normalizing words based on their WordNet synsets improve mfc.
